In the past few years, a substantial research effort has been devoted to the problem of Simultaneous Localization and Mapping (SLAM). The term “map” in the field of SLAM generally refers to a spatial arrangement of observed landmarks or features. If these landmarks correspond to obstacle locations (such as the measurements collected with a Laser Range Finder), then the “map” yields an occupancy map denoting the floor plan of the space in which the robot is operating. In other cases, in which the landmark information does not correspond to obstacle locations (such as the measurements taken with a camera), the “map” estimated with SLAM techniques is dissociated from the locations of obstacles (occupancy map). However, an occupancy map is required for the robot to properly make decisions and navigate the environment.
A number of SLAM techniques have been proposed for simultaneously estimating the poses (i.e. localization) and building the map. Some methods re-estimate past poses instead of only the latest pose as new information is collected, achieving an improvement in the estimate of the robot trajectory as the localization system is updated. Laser scans, for example, are collected as a mobile robot moves through an indoor environment. These scans are combined with odometry information to estimate the robot's trajectory to yield a map showing the floor plan of the building. As more information is collected, the accuracy of the map improves because the estimates of the past poses of the robot are improved. A disadvantage of this system is that all sensor readings and their associated poses must be stored to allow the sensor data to be re-processed when new information arrives. This results in storage requirements that grow linearly with time. There is therefore a need for a localization and mapping technique that efficiently creates an occupancy map using new information to improve accuracy of the map without the storage requirement growing linearly with time.